Cryptocurrency Data Visualization using Plotly Express

Switch to GPU mode for faster Computation (Runtime> Change runtime> GPU)

Importing all the necessary libraries

import pandas as pd     

coins_data = pd.read_csv("C://college//data visualization//final pap//Cryptocurrency-Visualization-main////Dataset/top_coins.csv")
coins_data

	Date	Open	High	Low	Close	Adj Close	Volume	Currency
0	10/1/2015	236.004	238.445	235.616	237.549	237.549	20488800	Bitcoin
1	10/2/2015	237.264	238.541	236.603	237.293	237.293	19677900	Bitcoin
2	10/3/2015	237.202	239.315	236.944	238.730	238.730	16482700	Bitcoin
3	10/4/2015	238.531	238.968	237.940	238.259	238.259	12999000	Bitcoin
4	10/5/2015	238.147	240.383	237.035	240.383	240.383	23335900	Bitcoin
...	...	...	...	...	...	...	...	...
35733	9/26/2020	68.514	69.631	68.514	69.184	69.184	298621240	Dash
35734	9/27/2020	69.183	69.885	67.430	68.749	68.749	284471075	Dash
35735	9/28/2020	68.749	70.044	67.517	67.521	67.521	296838687	Dash
35736	9/29/2020	67.511	68.794	67.040	68.659	68.659	286638250	Dash
35737	9/30/2020	68.596	69.109	66.886	69.109	69.109	266434156	Dash

35738 rows × 8 columns

from pandas_visual_analysis import VisualAnalysis
VisualAnalysis(coins_data)

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import scipy.stats as ss

sns.set_style('white')
sns.pairplot(coins_data,  hue='Currency')

coins_data.plot()

Data Visualization using Violin plots, Pie chart and Box plot

bitcoin_data = pd.read_csv("C://college//data visualization//final pap//Cryptocurrency-Visualization-main////Dataset/bitcoin_data.csv")
bitcoin_data

	Date	Open	High	Low	Close	Adj Close	Volume
0	2014-09-17	465.864014	468.174011	452.421997	457.334015	457.334015	2.105680e+07
1	2014-09-18	456.859985	456.859985	413.104004	424.440002	424.440002	3.448320e+07
2	2014-09-19	424.102997	427.834991	384.532013	394.795990	394.795990	3.791970e+07
3	2014-09-20	394.673004	423.295990	389.882996	408.903992	408.903992	3.686360e+07
4	2014-09-21	408.084991	412.425995	393.181000	398.821014	398.821014	2.658010e+07
...	...	...	...	...	...	...	...
2201	2020-09-26	10702.237305	10778.500000	10682.082031	10754.437500	10754.437500	1.810501e+10
2202	2020-09-27	10752.939453	10804.732422	10643.458008	10774.426758	10774.426758	1.801688e+10
2203	2020-09-28	10771.641602	10949.123047	10716.676758	10721.327148	10721.327148	2.272037e+10
2204	2020-09-29	10712.462891	10858.939453	10665.344727	10848.830078	10848.830078	2.045987e+10
2205	2020-09-30	10845.411133	10856.528320	10689.670898	10787.618164	10787.618164	2.075962e+10

2206 rows × 7 columns

plt.xlabel("Open")
plt.ylabel("Close")
plt.scatter(bitcoin_data["Close"], bitcoin_data["Volume"])

<matplotlib.collections.PathCollection at 0x1a69c1aac08>

coin_data_500 = coins_data[:5000]
coin_data_500['High']

0       238.445
1       238.541
2       239.315
3       238.968
4       240.383
         ...   
4995    104.844
4996    111.334
4997    120.640
4998    119.498
4999    119.133
Name: High, Length: 5000, dtype: float64

import altair as alt
from altair.expr import datum, substring

alt.Chart(coins_data[:5000]).mark_point().encode(
    x='Low',
    y='High',
    color='Currency:N'
).properties(
    width=650,
    height=400
).project(
    type='albersUsa'
)

Data Visualization using Scatter plots

import plotly.express as px

fig = px.scatter(coins_data, x="Open", y="Date", color="Currency")
fig.show()

Crypto specific Data Visualization- Analyzing Bitcoin using Line graph, Area graph and Candlestick charts

fig = px.histogram(coins_data,x="Date", y="Volume", color="Currency", marginal="box",
             height=400)
fig.show()

def make_example(selector):


    return alt.Chart(coins_data[:5000]).mark_rect().encode(
     x="High", y="Low", color="Currency"
    ).properties(
        width=600,
        height=400
    ).add_selection(
        selector
    )

interval = alt.selection_interval()
make_example(interval)

df_bitcoin = pd.read_csv("C://college//data visualization//final pap//Cryptocurrency-Visualization-main////Dataset/bitcoin_data.csv")
px.line(df_bitcoin, x= "Date", y = "Close")

px.area(df_bitcoin, x = "Date", y = "Volume")

import plotly.graph_objs as go  
fig = go.Figure(data = [go.Candlestick(x = df_bitcoin['Date'],
                                       open = df_bitcoin['Open'],
                                       high = df_bitcoin['High'],
                                       low = df_bitcoin['Low'],
                                       close = df_bitcoin['Close']
                                       )]) 

fig.show()